Dimensions of electrical distribution network datasets have been increasing exponentially as result of global acceptance of toward smart metering projects for secure implementation of demand response strategies and attaining satisfactory operation of electrical distribution network. Traditional approaches of analyzing these datasets have often been prone to losing of important information for instance averaging and aggregating of data; such loss of information can prove imperative in this era of demand side management and demand response. High dimensionality of distribution dataset is prominent factor for popularity of such conventional perspective toward large datasets. However, recent evolution in data mining have tossed various dimensionality reduction techniques expressing minimal loss of information. This paper proposes a feature based clustering algorithm aimed at dimensionality reduction, load profile characterization and probabilistic load variation assessment as a case study for smart village project of Nana Kajaliyala village, Gujarat, India. Proposed algorithm attains profile characterization using classical k-means alongside an empirical feature selection countering high dimensionality. A comparative evaluation of proposed algorithm with other popular techniques like self-organizing map (SOM) and classical k-means is presented in this paper. Moreover, a novel probabilistic analysis approach is conferred, which is directed at assessment of load variation, peak risk analysis of individual consumers. Determined statistical assessment measures in this paper would aid the utility with capability to execute cognitive decision making and reduce aggregate technical and commercial losses. Furthermore, load labels assigned to each characteristic profile could help managing load requirements, and planning future operations.

配电网络数据集的规模已呈指数级增长，这是全球对智能计量项目的认可，以确保安全实施需求响应策略并获得令人满意的配电网络运行。分析这些数据集的传统方法通常容易丢失重要信息，例如数据的平均和汇总。在需求侧管理和需求响应的时代，这种信息丢失可能成为当务之急。分布数据集的高维度是这种传统观点对大型数据集流行的重要因素。然而，数据挖掘的最新发展已经抛弃了各种表达维数最小的降维技术。本文提出了一种基于特征的聚类算法，以降维，负载轮廓表征和概率负载变化评估为目标，以印度古吉拉特邦Nana Kajaliyala村智能村项目为例。提出的算法使用经典k均值以及针对高维的经验特征选择来实现轮廓表征。本文对提出的算法与自组织映射（SOM）和经典k均值等其他流行技术进行了比较评估。此外，提出了一种新颖的概率分析方法，该方法旨在评估负载变化，单个消费者的峰值风险分析。本文中确定的统计评估措施将有助于公用事业公司执行认知决策并减少总的技术和商业损失。此外，分配给每个特性曲线的负载标签可以帮助管理负载需求并计划未来的运营。